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Unlocking deep eutectic solvent knowledge through a large language model-driven framework and an interactive AI agent
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DOI：10.1016/j.gce.2025.05.006.png)
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Abstract
En 中文
• An LLM-driven framework was developed for automated extraction of DES-related data. • Extraction of 34,027 records and 9,215 unique formulations from 14,602 articles was achieved with over 90% accuracy. • An AI agent was integrated with a graph-based retrieval system to enable interactive querying. • A structured DES knowledge base was constructed to accelerate formulation discovery in green chemistry.
Keywords:
Artificial intelligence
Large language model
Deep eutectic solvents
Text mining
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